Data.
Two datasets are used in the experimental evaluation, one un-labeled data with synthetic labels and one real data with real labels. SIFT1mhttp://corpus-texmex.irisa.fr/ has 1 million 128-dimensional vectors and does not have labels for each data vector. We cluster the vectors using k-means [1]} (\(k=10\) ) into 10 clusters and assign the corresponding cluster id as the label for each vector. The randomness of the label is also studied. Consider \(R\%\)  randomness, when we assign the label for each vector, we have a probability \(R\%\)  to set a random label in \(\lbrace 1,2,\cdots ,10\rbrace \)  instead of its corresponding cluster id. We have \(0\%\) , \(50\%\) , and \(100\%\)  random labels on SIFT in the following experiments. The dataset with real labels we used is MNIST with 784 dimensionshttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#mnist8m. We use the first 1 million vectors in this paper to have the same base vector size as the SIFT dataset.
